HIGH PRECISION DEM GENERATION FROM SPOT STEREO IMAGERY BY
OBJECT SPACE LEAST SQUARES MATCHING
K. C. LO , N. J. MULDER
LT.C.
P.O. Box 6, 7500 AA Enschede
The Netherlands
(ISPRS Commission III)
Abstract:
For automatic DEM generation from multiple view SPOT imagery with high precision, an approach
of "Refinement from Coarse" is proposed.
In the coarse DEM generation stage, in contrast with
the traditional methods which match at intensity level in image space with the signal
processing technique, the concept of Knowledge Engineering is used to perform high level
Feature Matching by Property List and String Matching for Correspondence analysis with high
reliability.
In the refinement stage, the Object Space Least Squares Matching method, which
is the most rigorous method from a theoretical viewpoint, is proposed for coarse OEM
refinement. This method is different from the traditional two-step approach which matches the
corresponding point in image space first, and then determines the DEM by Space Intersection;
this method is improved by back mapping the image data into object space to get object
reflectance O(x,y) with referring the object surface Z(x,y), and perform matching in Object
space. It is simultaneously to determine two functions in the object space: the terrain relief
Z(x,y) and the terrain reflectance O(x,y) in one solution with least squares adjustment
iteratively.
The disadvantages of matching in image space which the multi view image of the
same object has different geometric or reflectance distortion can be avoided. It's flexibility
allows the user to handle more than two SPOT multi-view images in one solution, which increases
accuracy and reliability as well. It is good for SPOT images which offer the resolution with
10 meters groundel size only.
Key Words: OEM, Correspondence Analysis, Property List, String Matching,
Object Space Least Squares Matching.
I.
INTRODUCTION
automatic generation of coarse DEM data by Linear
Feature Matching.
In order to plan and execute
complicated sequence of operations and functions,
we believe the methods of Knowledge Engineering
should be used, and correspondence Analysis will
be based on object detection with geometric definition and object description by means of Property
Lists [Mulder et al.,1988]. The selection / representation and use of proper knowledge is a central
problem in research. A range of different knowledge representation techniques must be developed,
along with a number of approaches to applying
knowledge, which are concerned in .the field of
Meta-Level Knowledge.
The generation of a GIS for a 3-D object oriented
data base is required urgently in many countries.
The automatic extraction of 2.5-D information from
SPOT stereo images is, potentially, an efficient
and economic way.
Some on-line (real time) commercial systems have appeared in prototype, but
the methods of the off-line system for reconstructing the earth surface with high quality and
acceptable economy must be researched and further
developed still; We want to establish a system for
generating DEM with high accuracy / high quality
/ high reliability with the support of the method
of Object Space Least Squares Matching.
It will
then offer good fundamental 2.5-D information to
GIS for mUlti-purpose applications.
2.
2.1
2.3
Traditional matching in Image Space has the shortcoming that the same object in multi-view images
appears with different geometric distortions and
radiometry. The geometric distortions are caused
by the central perspective which produces relief
displacement in the image; the tilt of the sensor
(SPOT has off nadir angle from a to 27 degrees)
causes tilt displacement in the image.
As the
multi-view SPOT images are taken at different
positions, at different times or under different
illumination conditions, this produces different
radiometry for the same object in different
images. These geometric and radiometric differences produce matching failures or reduce the
accuracy.
Therefore, motivation for improved
developments should come from the realization that
all information in images is inherent
in
the
object
space, and the transformation of the
matching problem from Image Space to Object Space
leads to a unified and precise approach where
all
available
knowledge is referenced to the
same basis. Research has to be carried out into
ways of how to model the surface of the terrain
and its reflection which is suitable for matching
properly and efficiently.
On the other hand, high accuracy can be obtained
by using Minimum Cost Matching; e.g. the Minimum
Euclidean Distance can be selected as the "Cost"
in Euclidean space for similarity assessment.
Because, in case the distance is small, the distance behaves as SIN function of the angle which
is between the Feature Vectors for matching, it
BACKGROUND PROBLEMS
SPOT Satellite Imagery
Since the SPOT imagery is acquired by push-broom
scanners, the imaging geometry is different from
the conventional central perspective photographs
with frame camera, and the orbit parameters
require simulate dynamic modelling.
For solving
the inverse camera model problems, the point in
issue is how to determine the orientation parameters of each scan line with sufficient accuracy
(e.g. improving the accuracy of Tie Point measurement/transfer and best pattern of Ground Control
Point distribution) and at lowest cost (e.g.
minimum number of Ground Control Point's). On the
other hand, the CCT of SPOT not only presents
imagery data, but also ot'fers special on board
auxiliary data, such as information about position, attitude, look direction,
radiometric
calibration of scene/sensor [SPOT User's Handbook,
1988]; how to fully use this information for
obtaining benefits in Aerial Triangulation ( A.T.)
and DEM generation stage must be considered in
every phase of image data processing.
2.2
Object Space Minimum Cost Matching
Knowledge Engineering
The existing methods of signal correlation and
feature matching are limited for handling some
special correspondence analysis problems, such as
133
may offer a sensitive measure for the distinction
of extreme similarity in the primitive feature
space.
For the Least Squares Matching method,
although the approach is completely different
(i.e. not searching / comparing and selecting, but
simultaneous Solution / determination of the
unknowns), the basic idea is similar, namely,
matching with very high accuracy to the degree of
subpixel. This also has the advantage that Least
Squares Adjustment is flexible which allows the
users to perform matching with more than two
images which can increase accuracy and reliability, and offers the theoretical quality estimation
of the result as well.
3.
3.1
technique, in order to solve the problem of high
correlation between orientation parameters caused
by narrow FOV of SPOT (4.125 degrees only) [Chen
& Lee,1989] [Shibasaki, el at.,1988]. At the same
time, we try to reduce the number of unknown orbit
parameters (e.g. simulating the orbital model for
position by 2nd degree polynomial, and linear
polynomial for attitude) resulting in a reduced
number of GCP, and aim at finding out the best
distribution of position of GCP in the adjustment
of A.T. providing sufficient accuracy for later
matching.
d) Application of Object space Least Squares
Matching with exterior orientation parameters as
unknown; using on board auxiliary data as initial
value, try to perform highly accurate Tie Point
Transfer with interaction in adjustment of A.T. by
iteration.
This is the most difficult part to
solve, because the known exterior parameters are
the back bone of Object Space Least Squares Matching.
If we treat them as unknown with on board
data as initial values instead, we need to know
how good the initial value should be to make the
iteration convergent.
THE SYSTEM COMPONENTS OF THE APPROACH
Preprocessing
3.1.1 Coordinate System Transformation
a) For mapping purposes, the coordinates of Ground
Control Point (GCP) are offered in the Mapping
System, because a SPOT image covers an area of 60
km x 60 km, hence the effect of the earth curvature can not be neglected any more. Therefore, a
transformation from the Map Coordinate System to
the Geographic System, and further to the Geocentric System is necessary. However, as the number
of digits of coordinates in the Geocentric System
is large, and.a Double Precision is required in
the data process to avoid truncation error, it is
necessary to transfer the coordinates to Local
Tangent Plane System to reduce the number of
digits, and thus to save memory and speed up processing.
e) Empirical accuracy study of exterior orientation parameters from A.T. with the application of
previously mentioned techniques, trying to get
sufficient accuracy to meet the requirement of
Object Space Least Squares Matching.
3.2
Coarse DEM Generation by Correspondence
Analysis with Property List
a) There are several methods to enhance the Linear
Features ( Line / Edge ) and then extract them;
however I the original position and intensity of
linear features should not be changed if the
features will be used for matching (not for visual
satisfaction) later.
Therefore, a non-linear
filter,
such as Conditional Rankorder Filter
[Mulder & Sijmons, 1984], can be selected for
segmentation; thereby the enhancement of features
is done by smoothing the background (suppressing
the minor features / noise also) and keeping
distinguished Linear Features· in their original
situation.
b) For using the on board data which are in the
Geographic System / the Geocentric System / the
Local Orbit Reference System / the Local Attitude
Reference System, transformation to the Local
Tangent Plane System is needed also.
3.1.2 Region Matching for DEM Generation and
Change Detection Because the matching would fail
within the homogeneous intensity region, or in the
regions which the land cover has been changed when
the satellite stereo pairs are taken with a long
interval of time.
Therefor, we use Conditional
Rankorder Operator to smooth the intensity within
the region first, then start Region Growing for
image segmentation, the boundary of region can be
extracted, and the shape can be described by V-s
Curve, combine with other properties of region,
such as the area, the position of gravity centre,
etc., to form a Property List. The initial probability of region matching can be obtained by
minimum cost function with the weighting properties in the list. Then the matching probability
are being adjusted by Relaxation Processes. until
the final conjugated region pairs are determined.
The elevation of the region can be calculated with
the conjugated region, and the change detection
can be done by checking the mismatching regions
and comparing the intensity between the conjugated
region after region matching, the matching failure
problem can be solved in these area [Lo,1992].
b) Reduce the 2-D search to a 1-D search during
matching, by the resampling of image data into
parallel line pairs (the approximate Epipolar Line
pairs). This is, however, more difficult to apply
to SPOT images, because the orientation parameters
are a function of time [Otto,1988] [Zhang & Zhou,
1989] •
c) Apply a Gradient Filter to the image and detect
the Linear Features with Zero-Crossing.
d) Property List Formation by collecting the
properties of Linear Features such as Position,
Amplitude and Shape of peak / valley in intensity
profile along the parallel line pairs [Lo,1989],
and the Orientation of Linear Features [Kostwinder
el at, 1988] •
3.1.3 Aerial Triangulation ( SPOT Orbit
Determination)
a) On board data are used to define the overlap
area of multi-view images and to choose the Tie
Points at the proper position and evaluate /
select the specific image properties (e.g. high
contrast in X and Y direction) Le. the most
suitable features for matching required for automatic point transfer. After image segmentation is
performed, the crossing of lines / edges are
detected as GCP, and sufficient properties (structure representation) of GCP can be obtained for
GCP identification by the Line-Based Structure
Matching Method or Chain-Coding Matching.
Moreover,
on
board
auxiliary
data
and
ground
coordinates of GCP are also used to help automatic
identification of GCP with property list for the
correspondence analysis between maps/photos.
e) To offer the criterion for Correspondence
Analysis, Cost Function Modelling is required by
assigning different weights to the individual
properties according to it's reliability and
major/minor contribution to express the characteristic of feature. The weight can be assigned by
prior analysis or by experiment with Trial and
Error.
f) Between the conjugated parallel line pairs, the
corresponding linear feature can be extracted by
String Matching which selects the Minimum Cost as
best matching, based on information from the Cost
Function. For the conventional matching strategy,
the Target Area of the left image is selected to
search for the best match in Search Area of the
right image only; the result may be different,
however, if the matching is from right to left.
The String Matching uses the mutually matching
strategy which matches not only left to right but
also right to left, then selects the real Minimum
Cost among them as best matching with the marking
technique for extracting them.
It increases the
reliability of the result.
If we confirm the
extracted linear features again by checking the
b) Establish the model for simulating the short
arc orbit of SPOT as a function of time [Konecny
et al. ,1987) [Kratky,1988).
c) Extract on board data from CCT of SPOT [SPOT
User's Handbook, 1988) and use them as constraint
(e.g. the attitude data) by the Pseudo Observation
134
continuation of linear features between neighbouring parallel lines,
the reliability can be
increased still more [Lo, 1989] .
The extracted
corresponding linear features pairs can be used to
generate coarse DEM, the major requirement of
generating coarse DEM with high reliability is
fulfilled.
3.3
iteratively [Wrobel,1988] [Helava,1988] [Ebner &
Heipke,1988] [Heipke,1990].
By this way, the
digital image matching, DEM generation and orthophoto computation have been combined into one
approach.
The main principle of Object Space Least Squares
Matching is as follows:
The basic Observation Equation of every pixel
within the patch for matching by the Least Squares
Adjustment is:
Refinement of Coarse DEM Data by Object Space
Least Squares Matching
L=
R
D*
v
3.3.1 The Illumination and Reflection Model for
facet stereo matching
There are three major
phenomena of the reflection : Ambient reflection,
Diffuse
(Lambertian)
reflection and Specular
(Mirror) reflection.
It describes the relationship of reflected radiance of a small facet of the
surface, the specific viewing direction, the complex illumination of the scene and light reflecting properties of the material.
(Weisensee,1988)
proposed three models of light reflection which
can be used for Object Space Least Squares Matching, they are derived from general model as follows:
L R)
cose - D( Z, A,
(8)
f
v
noise or residual error in intensity
the unknown intensity value assigned to
one groundel
e
the angle between the surface normal and
the direction of sun
D( ): back mapping the image intensity value of
corresponding pixel to the groundel.
Z
the heights of N x M grid points (DEM) to
represent the terrain surface Z(X,Y) which
can offer the height in any position by
bilinear interpolation.
A
orientation parameters of the sensor
L
illumination model
R
object reflectance model
a) The influence of L & ~ can be simplified ey
local linear radiometric correction with offset 81
and gain 52 (But not for close range photogrammetry and large scale photo).
Therefore, (8) can
be:
D
(1)
the reflected radiance cause by incident
radiance of illumination
the ambient component from half space L 1a
and the semi-spatial reflectance Ra
n light sources having irradiance LIn
the surface normal
the direction to light source n
the apparent solid angle of incident
radiance from a particular light source
the diffuse reflectance Rd and its fraction
of proportion for diffuse reflectance
the specular reflectance R and its fraction of proportion for spe~ular
reflectance
v
D*
cose - 51 - 52
*
(9)
D(Z,A)
For the patch of first image, 81 and 82 can be
assumed as constant I they are unknowns for the
patch of second, third and further images.
b) For Point Transfer in A.T., Z and A are
unknowns also,
expanding the
nonlinear
part
(82 * D(Z,A»
of (9) !:o li~ear increments from
approximate values: ( S1 , S2 ) 0 = ( 0 ,1 ), we
obtain:
In our case, we can assume that there is only one
light source (n=l,the sun) to be considered. The
amount of light reflection which independently
with the viewing direction is :
v
The
the
the
and
(2)
the specular Component which is rare exist and can
be omitted is:
(3)
the relationship between recorded intensity G; of
discrete pixel i by the sensor and the reflected
radiance from the terrain surface can be a linear
transformation:
= D*
cose - 81 - 82
* D(Z,A)
- 82
*
[(aD/aZ)o dZ -(aD/8A)0 dA] _oD(Z,A~o dS2
(10)
coefficients indexed by 0 are calculated with
estimated values which will be updated during
iteration; Expanding the coefficient (aD/aZ)o
(aD/aA)o of the design matrix:
(aD/aZ)o
(aD/ax .ax/aZ)o+ (aD/ay .ay/az)o (11)
(aD/aA)o
(aD/ax .aX/aA)o+ (aD/ay .qy/aA)o (12)
Because aD/ax and aD/ay represent the gradient of
the intensity, it can be used for a quality estimation (selection), and provide the different
weights accordingly in Least Squares Adjustment.
(4)
c) In case of DEM generation after A.T •• The
orientation parameters of sensor A are known
already. Therefore, the observation equation for
each pixel within the matching pat~h is:
if the terrain surface is perfect ambient reflection, the first reflection model can be:
(5)
v
D. is the reflected radiance of facet i, if the
t~rrain surface is assumed as perfect Lambertian
reflectance, the second model is:
G;= a j + b(cos(NQ.lDQ D; = a j + bfcose;Q D;
Q
D;
cosO - 81 - 82
* D(Z,A)o - 820 *
(13)
(aD/aZ)o dZ - D(z,R)o Ds2
!f we want to obtain DEM Z(X,Y) only, the unknown
D can be eliminated (Reduced Normal Equation)
during the inversion of the normal equation to
save calculation time.
(6)
the third model:
G; = a j + b k
= D*
3.3.3 The characteristics of Object Space Least
Squares Matching
a) We perform the matching in Object Space instead
of in Image Space r because the disadvantage. of
matching in image space would be that the multi
view image of the same object would have different
intensity or shape caused by different relief
displacement / tilt displacement / different
illumination situation / different reflection
effects from different positions of the sensor
relating to different relief of object etc.,
resulting in matching failure or poor accuracy.
(7)
results from applying the transformation (4) to
facets instead of windows. It means a; is still
valid for window jf the b k applies to a bilinear
height/reflection facet k respectively.
3.3.2 The orinciple of Object Space Least
Sguares Matching Back mapping of image data into
object space to get object reflectance D(x,y)
(image inversion) by referring the object surface
Z(x,y) which is approximate at the beginning, and
perform matching on the object surface. It simultaneously determines two functions in the object
space: the terrain relief Z(x,y) and the terrain
reflectance D(x,y) with Least Squares Adjustment
b) High accuracy can be achieved by using the
Minimum Cost Matching which is based on the Theory
of Minimum Cost Sequence of Error Transformation.
This method may select the Minimum Euclidean
135
Distance as the "Cost" in Euclidean Space for
similarity assessment.
Because the distances
behave as SIN function of angle between the feature vectors in case the distance is small, it
offers a sensitive measure for the distinction of
close similarity in the primitive feature space.
In the Least Squares Matching Method, though the
approach of matching is totally different, the
basic idea is similar (Min. ~vv <--> Min. distance), namely matching with very high accuracy to
the degree of subpixel; it requires, however, a
very good conjugacy prediction (coarse DEM). The
commonly used Normalized Cross Correlation method
provides a correlation function behaviour as a COS
function [LO,1991]. This function has a flat peak
at maximum similarity; therefore, to interpolate
this function to get subpixel accuracy is hardly
worth the effort.
The other way is called Indirected Pixel Transformation: we start with a coarse DEM grid
(X,Y,Z), and the Collinearity Equation with orientation parameters of the scanner is used to get
the corresponding pixel position (x,y).
Another
problem arising from SPOT's Push Broom scanner is
that the orientation parameters of each scan line
are different (for a frame camera, it is the same
in the whole image); therefore, if we don't know
which set of orientation parameters we should use
for
transformation,
we
can
not
use
the
Collinearity Equation to get the corresponding
pixel on the image and transfer its intensity
(after resampling) to that grid point. If we can
solve this problem, the Indirected Pixel Transformation is better than Directed Pixel Transformation, as the weighted average of pixel intensities which are obta~ned from multi-view images
according to the same grid point, can be used as
the estimated object reflection D (X, Y), and the
weight is assigned according to the slope of ray.
c) When we use the unknown parameters to establish
a mathematical model (Observation Equations) which
can precisely describe the phenomena of observation/ sampling (e.g. the back mapping of intensity
from image space to object space), the Least
Squares Method minimizes the differences between
the description model with actual model to determine the value of unknowns ( when the observation
equation is linear ), or to update the estimated
values of unknowns iteratively (when the observation equations are non-linear).
Every pixel
involved in matching can provide one observation
equation; if redundant observations exist (the
number of observation equations is more than the
unknown parameters,
i.e.
the over-determined
problem), the Least Squares Method is a good
method to determine the value of unknown parameters even when the errors of observations are not
a Normal Distribution.
The principle of the
Maximum Likelihood Method is identical with the
principle of Least Squares Adjustment, if we have
assumed that the errors of observations are a
Normal Distribution.
But unlike the Maximum
Likelihood Method of estimation, the method of
Least Squares does not require the knowledge of
distribution from which the observations are drawn
for the purpose of parameter estimation; however,
for testing of hypotheses, we would require the
knowledge of the distribution [Bouloucos,1989].
f) The Least Squares Matching method is capable to
handle any number of images over two, e. g. the
Triplet, as well as images scanned in various
spectral bands simultaneously. It increases both
reliability and accuracy of the result [Shibasaki
& Murai,1988].
g) The traditional method uses windows of pixels
for matching to determine a single point (usually
the middle point) only, but Obj ect Space Least
Squares Matching uses a window of pixels for
matching to determine multi-points in a grid
pattern DEM in one solution.
If preprocessing
provides prior knowledge about the quality of
matching windows (e.g. the gradient of intensity),
we can assign different weights accordingly (e.g.
give the high contrast pixels a larger weight) in
the Least Squares Adjustment. This means that we
require the high contrast pixels to offer a larger
contribution for the decision making which helps
to avoid making the wrong decision in the homogeneous part of the image. Thus, a combination of
advantages from Feature Based Matching and Area
Based Matching can be obtained. DEM determination
executed in this way is thus called Multi-Point
Matching, and offer higher reliability [Rosenholm
, 1987b].
d) Least Squares Window Matching has been used in
the two-step approach whereby matching in image
space is performed to get the conjugated position
first, then the Space Intersection is used to
reconstruct the object surface [Ackermann, 1984 ] .
Its window size is limited to 20x20 pixels or
30x30 pixels; if the window is smaller than this,
reliability is decreased, but if it is larger than
this, accuracy will be poor because matching is
performed on image space, and the geometry model
is simplified [Rosenholm,1987a]. The improved
approach unifies these two steps into one, and
matching on object space.
Back mapping of image
intensity into object space to get object reflection D(X,Y) (image inversion) is done by referring
the coarse object surface Z (X, Y), then perform
matching on object surface and refine the coarse
object surface iteratively. In addition to this,
two functions in the object space, i.e. the object
surface Z(X,Y) and the object reflectance D(X,Y)
are simultaneously determined (considered) in one
solution with Least Squares Adjustment; this is
the reason why we consider this to be a more
rigorous method than the earlier methods.
h) Robust Estimation techniques can be applied in
Least Squares Adjustment to get rid of noise as a
gross error of sampling (observation).
i) The Least Squares Method provides theoretical
quality estimation of the matching result based on
statistics theory. It also offers useful information for cleaning gross errors in DTM data in the
postprocessing stage as quality control, as well
as for using the DTM data in GIS [Day & Muller
,1988] .
j) The grid DEM is separately generated
patch with the quality estimation.
The
tion of the whole DEM can be done, e.g.
of the Finite Element Method, with a
improvement of DEM in the last stage.
al., 1988].
patch by
aggregaby means
quality
[Xiao et
4. SUMMARY
4.1
Summary of the Matching
Selection of Approach
Algorithms
and the
There are many matching algorithms that can be
used; we summarize the relevant algorithms, and
indicate those selected and applied in this system
with the mark of ***.
e) By referring the coarse object surface Z(X,Y),
there are two ways to back mapping of the image
intensity into the object surface in order to get
the estimated object reflection D(X,Y).
One is
called Directed Pixel Transformation: it starts
with pixel position (x,y) in the image; the
Collinearity Equation with orientation parameters
of its scanner is used to intersect the coarse
object surface Z(X,Y) to get the position of
corresponding groundel X,Y, and transfer the
intensity of this pixel to it. However, the problem is that the groundels distribution on object
surface after back mapping present a random pattern and they are different in different multiview images also; another shortcoming is that the
height of these random position points needs to be
interpolated to grid DEM but lose information.
(a) Information for Matching:
*** Intensity-Based
*** Feature-Based (Property-Based)
(b) Criterion for Similarity Assessment:
* Angle between Matching Vectors:
COS Function --->
Less Accuracy/ High Reliability
*** Distance between Matching Vectors:
SIN Function(in small distance) --->
High Accuracy / Low Reliability
136
(c) Matching strategy:
stereo compilation of mapping can intervene.
Therefore, if we can establish a Knowledge Base in
which there is knowledge such as the well trained
operator has, and incorporate it into the off-line
system as an Expert system, we may obtain a similar capability as the on-line system with an
operator.
Hereby, we need to implement our
approach to the computer in a semi-automatic /
interactive mode with human interference in order
to gain more experience and enough knowledge to
enter into a Knowledge Base; then the problems of
automatic DEM generation can be solved by an
Expert System with sufficient knowledge in its
Knowledge Base.
Some primary experiments have
been done for system start up/ interior orientation/ relative orientation in the measurement
stage to perform diagnostics during the process
[Kretsch, 1988]; there is still a long way to go
for handling the whole system by a mature Expert
System. Therefore, we have to start implementing
our approach in the computer to accumulate our
knowledge and improve the Expert System.
* One way Search / Compare and Select from
Target Window to Search Window
*** Two ways (mutually) Search / Compare and
Select between each other
*** Solve / Determine the Unknowns
* Multi-Pixel Matching ---> one point
determination
*** Multi-Pixel Matching---> Multi-points
determination ( Weighting the good / high
contrast pixels to help the poor pixels
result in higher Reliability )
(d) The Minimum Cost Sequence of Error
Transformation:
* City Block Distance (Min. Absolute
Difference)
*** The Min. Euclidean Distance in Euclidean
Space(Isotropic) (The Weighted Min. Distance
in Feature Space)
.
*** The Least Squares Adjustment(~vv --> M~n.)
with unequal weight
REFERENCES
summary of the process stages and their
purposes
4.2
[ 1]
preprocessing
.Coordinate System
Transformation from Map System
to Local Tangent Plane System
*Region Matching/OEM Generation
oja--
Board
Data
S
[2]
Tie Point Selection/
GCP Identification (Map/Photo)
[2a].Assessment for Good Image
[2b] . Structure Feature Detection
Structure Feature Representation
Structure Feature Matching
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[2] Bouloucos, T. [1989]: Applied Statistics and
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Least Squares
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[4] Day, T./ Muller, J.P. [1988]: Quality Assessment of Digital Elevation Models produced by
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[5] Ebner, H./ Heipke, C. [1988]: Integration of
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[7] Helava, U.V. [1988]:
Object Space Least
Squares Correlation. ISPRS 16th Congress, Kyoto,
Com. II. pp. 321-331.
[8] Konecny, G./ Kruck, E./ Lohmann, P./ Engel, H.
[1987]: Evaluation of SPOT Imagery on Analytical
Photogrammetric Instruments. PE&RS Vol. 53, No.9,
pp. 1223-1230.
[9] Kostwinder, H.R./ Mulder, N.J./ Radwan, M.M.
[1988] :
DEM Generation from SPOT Multiple View
Images using Correspondence Analysis. ISPRS 16th
Congress, Kyoto. Com.III, pp. 111.442-449 (B11).
[10] Kratky, V. [1988]: Universal Photogrammetric
Approach to Geometric Proceeding of SPOT Images.
ISPRS 16th Congress, Kyoto, Com. IV, pp. 180-189 .
[11] Kretsch, J.L. [1988]: Applications of Artificial Intelligence to Digital Photogrammetry.
Purdue University, Ph.D Thesis.
[12] Lo, K.C. [1989]:
Image Quality Assessment
for Automatic Production of DEM Data. Conference
of Photogrammetry Automation, Tainan, Taiwan. pp.
J1-J20.
[13] Lo, K.C. [1991]:
Review and Analysis of
Stereo Matching. Journal of Surveying Engineering.
Vol.33, No.3. pp. 23-42.
[14] Lo, K.C. [1992]:
The Automatic DEM Generation and Change Detection by Region Matching.
ISPRS 17th Congress, Washington,D.C., Com.III.
[15] Mulder, N.J./ Sijmons, K. [1984]:
Image
Segmentation using Conditional Rankorder Filtering. ISPRS 15th Congress, Rio de Janeiro. Com. III.
[16] Mulder, N.J./ Radwan, M.M. [1988]:
Correspondence Analysis applied to Image Matching and
Map Updating. ISPRS 16th Congress, Kyoto, Com. IV,
pp. IV.422-429 (B11)
[17] otto, G.P. [1988]:
Rectification of SPOT
Data for Stereo Image Matching. ISPRS 16th Congress, Kyoto, Com. III. pp. 635-645.
[18] Rosenholm, D. [1987a]: Empirical Investigation of Optimal Window Size using Least Squares
Image Matching. Photogrammetria (PRS),42, pp. 113125.
*Avoid:
Earth curvature ..
Double Precision
*Avoid Matching
Failure in
homogeneous area
*Apply Knowledge
Engineering to
increase the
degree of
Automation
*Offer
Orbit Parameters
with Min. No. of
GCP and best
~----------------------~
distribution of
[3a] . Bundle Adjustment with OnGCP.
'----/-----'1 Board Data as the Constrain
* Improve the
[3b] . using Object Space Least
Accuracy of
Squares Match with Orientation
Point Transfer
Parameter of Scanner as unknown
then improve the
to perform the highest accuracy
Accuracy of A. T.
Point Transfer interactively
also.
with A.T. by iteration
rbit
Data
[3]
Aerial Triangulation
/ Tie Point Transfer
[4]
f------+-
Coarse OEM Generation by
Feature Matching
*. Correspondence Analysis:
a. Image Smoothing
b. Image Rectification
c. Linear Feature Detection
d. Property List Formation
e. String Matching for Linear
Feature Extraction
* . Space Intersection for OEM
Determination
*Automatic
Generation of
Coarse OEM Data
with
High Reliability
[5]Refinement of Coarse OEM Datal *High quality
'I OEM Generat~on
• . Object Space Least Square
with
Matching
subpixel Accuracy
5.
CONCLUDING REMARKS
In general, computer stereo vision belongs to the
class of Ill-Posed inverse problem. Although the
Obj ect Space Least Squares Matching is the most
rigorous method with high precision from a theoretical viewpoint, but it is a serious Ill-Posed
Problem, and is difficult to implement in practice, therefore, every method to improve the
computational stability of image matching is very
important, e.g. try to include all available
geometrical constrains, such as the regularization
method to minimize the surface curvature is tried
to help the convergency of solution; the idea of
pyramidal approach is used to improve the problem
that the range of convergency is very small, etc ..
On-Line system for automatic DEM generation can be
used in the map production line because an operator is still involved.
It means that all the
matching algorithms still can not completely solve
all problems because the images of terrain and the
terrain itself are so complicated. However, when
matching has failed, a well trained operator who
has sufficient knowledge about problem solving of
137
[19] Rosenholm, D. [1987b]: Multi-Point Matching
Using the Least Squares Technique for Evaluation
of Three-Dimensional Models. PE&RS, Vol. 53, No.6,
pp. 621-626
[20] Shibasaki, R./ Murai, S. [1988]:
Improvement of Mapping Accuracy by Applying Triplet
Matching to SPOT Imagery. ISPRS 16th Congress,
Kyoto, Com. III. pp. III.264-273. (B10)
[21] Shibasaki, R./ Murai, s./ Okuda, T. [1988]:
SPOT Imagery Orientation with Satellite Position
and Attitude Data. ISPRS 16th Congress, Kyoto,
Com. III/I, pp. III.125-132. (B9)
[22] Weisensee, M. [1988]: Model of Light Reflection for Facet stereo Vision. ISPRS 16th Congress,
Kyoto, Com.III/4. pp. III.360-367. (B10)
[23] SPOT. [1988]:
SPOT User's Handbook. Vol.1
Reference Manual./ Vol.2 SPOT Handbook.
[24] Wrobel, B.P. [1987]: Facet Stereo Vision
(FAST Vision) - A New Approach to Computer Stereo
Vision and to Digital photogrammetry. Conf. on
Fast Processing of Photogrammetric Data, Interlaken, pp. 231-258.
[25] Xiao, J.Z / Liu, J.Y. [1988]: Digital Matching of SPOT Stereo Images by Finite Elements Least
Square Techniques. ISPRS 16th Congress, Kyoto,
Com. III. pp. III. 373-382. (B10)
[26] Zhang, Z .X. / Zhou, Y.Q. [1989] :
A New
Approach to Arrange the Approximate Epipolar Line
for SPOT Images. Special Issue of Seminar on the
Wang Zhizhuo' s Academic Thinking.
Publishing
House of Surveying and Mapping, Beijing. pp. 4650.
138